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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 199 records · Page 11
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NASA NTRS2019

Evaluation Analysis of NASA SMAP L3 and L4 and SPoRT-LIS Soil Moisture Data in the United States

Soil moisture has a critical role in the development, frequency and persistence of climatic and hydrologic extremes such as drought, heat wave and flooding events. In situ soil moisture data are uneven and sparse in time and space. This highlights the need to utilize other soil moisture sources to fill this spatiotemporal gap. The goal of this study is to validate one satellite-based and two model-based soil moisture datasets with in situ data across the United States. Soil moisture information from the Soil Moisture Active Passive (SMAP) enhanced level 3 (L3) (SMAP L3) and modeled level 4 (L4) (SMAP L4) data at 9-km resolution and Short-term Prediction Research and Transition-Land Information System (SPoRT-LIS) modeled at 3-km resolution were selected for evaluation. SPoRT-LIS is a near real-time, high resolution operational land analysis data. Ground-based data were obtained from the North American Soil Moisture Database (NASMD) for 362 stations. Seven statistical indicators including anomaly, Spearman, and Pearson correlation coefficients, the systematic error (Bias), root mean square error (RMSE), unbiased root mean square error (ubRMSE), and normalized standard deviation (SDV) were used to evaluate the satellite- and model-derived soil moisture data. In addition, the triple collocation (TC) error model was used to measure the error among SMAP L4, SPoRT-LIS and ground-based data. This study assesses which satellite or modeled dataset is most appropriate for specific times and locations to use as a surrogate for in situ observations. Temporal and spatial analysis demonstrated that, overall, SMAP L4 performed better than SMAP L3 and SPoRT-LIS. Strong agreement was observed between SMAP L4 and in situ observations (ρ = 0.53, Bias = −0.006) in all seasons and most regions with various land covers, especially in winter and in the central regions of the United States. For croplands, SMAP L4 presented the best agreement with in situ data, analyzing all period (ρ = 0.60) and non-winter period (ρ = 0.61) separately.

Tavakol, Ameneh↗
NASA NTRS2019

SMAP L4 Assessment of the US Northern Plains 2017 Flash Drought

A rapidly developing "flash drought" occurred over the US Northern Plains in the summer of 2017, spurred by unusually high temperatures and strong evaporative demand. The impacts of the drought included widespread reductions in rangeland and agricultural productivity that cascaded into significant economic losses. Here, we used satellite information from the NASA Soil Moisture Active Passive (SMAP) mission to clarify the nature and impact of the drought on regional vegetation growth. The model enhanced SMAP Level 4 Soil Moisture (L4SM) and Carbon (L4C) products were used with other ancillary data to examine spatial and seasonal anomalies in surface to root zone soil moisture and vegetation productivity (GPP). We find that the flash drought was triggered by a mid-July heat wave, conditioned by exceptionally low spring rainfall. The drought resulted in anomalous low soil moisture levels and regional GPP collapse, coinciding with severe (D3) to exceptional (D4) drought conditions indicated from the US Drought Monitor. The SMAP L4C GPP anomalies closely tracked reported county-level crop production anomalies for the major regional crop types, indicating generally larger productivity decline in managed croplands than surrounding natural areas. The SMAP L4 global products provide an effective indicator of vegetation growth changes and moisture-related restrictions on ecosystem productivity that are complementary with more traditional drought assessment tools.

GPP↗
NASA NTRSDate not supplied

Studying the 2019 Australian Bushfires Disaster using NASA Data: A Data-Driven Storytelling Approach

The 2019-2020 Australian fire season was particularly devastating, with millions of acres of land burned and impacts affecting Australian ecology, local populations and air quality, and the atmosphere. Australia saw unprecedented heat waves, with temperatures reaching 120 F (49.1 C) in January across central and eastern Australia. The fires gave rise to a host of atmospheric phenomenon, including smoke transport and lofting generated by storm-induced by fires, known as pyrocumulonimbus, reaching the stratosphere. NASA’s satellites not only tracked the event in real time, but also gathered data to further inform forecasting and response methods in the future. To better assist the public in understanding the lead up, impacts, and aftermath effects of these fires, the Science Outreach Team at NASA Langley Research Center’s Atmospheric Science Data Center (ASDC) Distributed Active Archive Center (DAAC) used Esri’s storymap tool to guide users through understanding relevant phenomenon, contributing factors, the effects this event has had on global atmospheric composition, and the science behind researching the tie between disasters and public health. The storymap uses data from the ASDC-supported NASA missions Measurements Of Pollution In The Troposphere (MOPITT), Cloud-Aerosol and Infrared Pathfinder Satellite Observation (CALIPSO), Clouds and the Earth’s Radiant Energy System (CERES), the Stratospheric Aerosol and Gas Experiment (SAGE III), and Multi-angle Imaging SpectroRadiometer (MISR). By using data-driven storytelling to communicate impacts of a large fire event, we hope to provide an accessible, engaging science outreach tool format.

Sanjana Paul↗
NASA NTRS2020

Studying the 2019-2020 Australian Bushfires Using NASA Data

The 2019-2020 season has been one of the worst fire seasons on record. Australia has seen unprecedented heat waves, with temperatures reaching 120 F (49.1 C) in January across central and eastern Australia. NASA's satellites not only tracked the event in real time, using resources such as the Global Actives Fires and Hotspots Dashboard you see below, but also collected large volumes of rich data that scientists and researchers can use to study the event and the regional and global effects of the disaster. In this Esri StoryMap, we will guide you through the factors leading up to the 2019-2020 Australian bushfires disaster, the effect this event has had on air quality and global atmospheric composition, and the science behind researching the tie between disasters and public health. This story map will use data from ASDC-supported NASA missions such as the Measurements Of Pollution In The Troposphere (MOPITT), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), Clouds and the Earth’s Radiant Energy System (CERES), Stratospheric Aerosol and Gas Experiment (SAGE III) on the International Space Station (ISS), and Multi-angle Imaging SpectroRadiometer (MISR).

Sanjana Paul↗
NASA NTRSDate not supplied

Updates of MERRA-2 Data and Services at NASA GES DISC

Over40 years of NASA climate reanalysis datasets from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) are available at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). In addition to being used in traditional weather and climate research, MERRA-2 is also widely used in application studies of, e.g., wind and solar energy, air quality and health, food and drought, and heat waves. Two new MERRA-2 datasets were recently added at the GES DISC: (1) climate statistics derived fromMERRA-2 daily data to assist in the analysis of extreme temperature and precipitation events and of large-scale meteorological patterns from 1980 to the present and (2) gridded satellite and conventional observations processed in the MERRA-2 system, along with key statistics derived from the data assimilation, to help better understand how the quality of observations directly affect there analysis data. The GES DISC focuses its efforts on continually improving existing data services and to develop new data tools to satisfy various user communities. The newly added features include the following: Time series service: This is a new service for MERRA-2 data, which enables the easy and fast access of long-term hourly or daily time series at a location for popular parameters. The data is saved in a single le in Ascii format with a user- friendly structure. New analytic functions in the subsetter interface: Options for downloading daily minimum and maximum values have been added into the subsetter interface, in addition to the existing daily mean option, for all MERRA-2 and MERRA sub-daily products. Data format conversion to GeoTIFF has been implemented. New variables in Giovanni: Most monthly variables have been integrated into Giovanni, GESDISC’s online visualization and analysis tool. Due to the large data volume, hourly variables were selected based on user requests. More online information: New MERRA-2 documentation has been added: Data How-To, Data in Action, and FAQ. This presentation overviews two new MERRA-2 datasets and illustrates the new features of data services through a number of case studies. MERRA-2 data and services can be found at: https://disc.gsfc.gov/datasets?

Data management↗
NASA NTRS2017

The Past and Future Changes in Climate of the Rice-Wheat Cropping Zone in Punjab, Pakistan

Agriculture is ranked top among Pakistan economic sectors vulnerable to the potential impacts of climate change. The agricultural production system is directly affected by weather inputs (temperature, solar radiation, and rainfall) that are projected to change in future (following increases in carbon dioxide and other greenhouse gasses). Although Climatic extremes such as drought, floods and heat waves, are expected to increase with detrimental consequences for agriculture and livestock production, yet changes in mean climates also pose challenges to sustainable development. This study presents climate change results for five districts within the major rice-wheat productivity zone of Punjab province in Pakistan. The results are focused on RCP8.5 mid-century (2040-2069) scenarios derived from five global climate models (GCMs) output and the Kharif (June-October) and Rabi (November-April) seasons. Analysis of recent historical weather data of Sialkot and Sheikhupura districts shows an increase in minimum temperatures and maximum temperatures and a large variation in rainfall. This temperature change and variability in rainfall are expected to enhance further as we approach 2050s. The mean maximum temperature is projected to increase by 2-2.5 ºC during the rice growing (Kharif) season and 2.4-2.7ºC during the wheat growing (Rabi) season. Rainfall during the rice growing season is more uncertain, with projections indicating an increase of 25%-35 % in the study region, while a minimal change is expected during the Rabi season. Climax of the rice growing season corresponds with the projected increase in monsoon intensity, leaving no doubt about the crop water demand satisfaction. However, a simultaneous increase in the day and night temperature may affect the growth and development of some critical phenological stages and thus could impact final yield.

Climate Change↗
NASA NTRSDate not supplied

The Abnormally Hot June 2021 in the Western U.S.: A MERRA-2 perspective

An extreme heat wave occurred over much of the western US and Canada in late June 2021. We use daily and monthly near-surface temperature from MERRA-2 Reanalysis to compare the hot conditions of this year with the previous 40 years, particularly over the northwestern US that was severely affected.

T2m↗
NASA NTRS2022

Multisensor Machine Learning to Retrieve High Spatiotemporal Resolution Land Surface Temperature

Climate change is making heat waves more frequent, long-lasting, and severe. While multiple satellite types provide data to monitor surface temperature, geostationary (GEO) sensors provide near-continuous, continental-scale observations which can better capture the diurnal variability of land surface temperature (LST) than intermittent observations from low-earth orbit (LEO) sensors. However, standard products from GEO satellites are available at coarsened spatial and temporal resolutions compared to the native sensor resolution. Using datasets from the NASA Earth Exchange, we leveraged co-located, co-temporal observations from LEO and GEO satellites to learn a data-driven mapping using a convolutional neural network. The resulting NASA Earth eXchange Artificial Intelligence LST (NEXAI-LST) achieved a mean absolute error of 1.73 K relative to the target LEO product and improves on both spatial and temporal resolution [2 km, 10 minute] compared to the GEO full disk standard product [10 km, hourly]. In validation against measurements from a ground-based sensor network, NEXAI-LST achieves similar or better fit than both LEO and GEO standard products, while depending none of the prior knowledge of land surface and atmospheric states required by physical-statistical models. Further, application of the model to unseen LEO and GEO satellites demonstrates robust generalization of the model across spatial region, time of day, and sensor. In support of NASA’s open-source science initiative, we make our NEXAI-LST product, model, and codes available to facilitate data exploration and further studies.

Kate Marie Duffy↗
NASA NTRS2021

Impact of Recent Climate Extremes on Mosquito-borne Disease Transmission in Kenya

Climate change and variability influence temperature and rainfall, which impact vector abundance and the dynamics of vector-borne disease transmission. Climate change is projected to increase the frequency and intensity of extreme climate events. Mosquito-borne diseases, such as dengue fever, are primarily transmitted by Aedes aegypti mosquitoes. Freshwater availability and temperature affect dengue vector populations via a variety of biological processes and thus influence the ability of mosquitoes to effectively transmit disease. However, the effect of droughts, floods, heat waves, and cold waves is not well understood. Using vector, climate, and dengue disease data collected between 2013 and 2019 in Kenya, this retrospective cohort study aims to elucidate the impact of extreme rainfall and temperature on mosquito abundance and the risk of arboviral infections. To define extreme periods of rainfall and land surface temperature (LST), we calculated monthly anomalies as deviations from long-term means (1983–2019 for rainfall, 2000–2019 for LST) across four study locations in Kenya. We classified extreme climate events as the upper and lower 10% of these calculated LST or rainfall deviations. Monthly Ae. aegypti abundance was recorded in Kenya using four trapping methods. Blood samples were also collected from children with febrile illness presenting to four field sites and tested for dengue virus using an IgG enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR). We found that mosquito eggs and adults were significantly more abundant one month following an abnormally wet month. The relationship between mosquito abundance and dengue risk follows a non-linear association. Our findings suggest that early warnings and targeted interventions during periods of abnormal rainfall and temperature, especially flooding, can potentially contribute to reductions in risk of viral transmission

Aedes aegypti mosquitoes↗
NASA NTRSDate not supplied

Resources at GES DISC for Agriculture Studies

● Introduction to GES DISC (Distributed Active Archive Center- DAAC) ○ Data Holding for Agriculture study ● Resources for Agricultural studies at GES DISC ○ Data Access ○ Data Services and Tools ○ Learning resources : ■ Studying seasonality ■ Studying extreme events: Floods, Flash Floods, Heat Waves, Drought ■ Data Recipe (How-To) ● Demo and hands-on

Zhong Liu↗
NASA NTRSDate not supplied

AgMIP Local, National and Global Assessments of Food System Challenges in A Changing Climate

Food systems connect a diverse network of stakeholders around the world, with complex dynamics governing production, processing, transportation, trade and consumption of food products. Extreme conditions can disrupt multiple components of the food system, with systemic connections providing structure that can both buffer and exacerbate risks. Climate change is shifting current practices in agricultural production and the broader food system, encouraging shifts toward more diverse value chains capable of drawing from multiple food producing regions to reduce the chance that an entire value chain be subject to simultaneous impacts. The IPCC Working Group I report assessed that heat waves, fires, floods and severe storms are becoming more pronounced and widespread with each degree of global warming, increasing food system risk. Food system disruptions can also come from outside the climate system, including from viral outbreaks, geopolitical conflict and socioeconomic changes. This presentation will highlight how the Agricultural Model Intercomparison and Improvement Project (AgMIP) develops modeling approaches that connect climate, biophysical and socioeconomic models in order to capture complex responses of the food system and potential interventions by diverse stakeholders in the public and private sectors. Models are applied on a range of scales that match decision contexts. These include households in specific agricultural production regions (e.g., within a country), national-level policymakers considering policies, sustainability and development priorities for a country’s agricultural sector, and global food market models that balance production and consumption around the world with competition for land, water, energy and sustainability priorities. Models are capable of capturing many important responses, but further development is needed to represent key systemic risks and the possibility for additional interventions beyond the farm gate. Improved food system models will provide important insights into our society’s ability to cope with climate change, as well as our ability to identify agricultural adaptation and mitigation opportunities to reduce overall risk.

Food systems↗
NASA NTRSDate not supplied

Applied Uses of MERRA Reanalyses: Current and Future Prospects

During the initial planning of the Modern Era Retrospective-analysis for Research and Applications (MERRA), the project was envisioned to become a tool for applied sciences and decision makers, as well as weather and climate research. MERRA was the first reanalysis to provide data at 1 hourly frequencies. After discussions with wind energy users, 50m above the surface winds were included in the data collection to begin to represent the environment at the turbine height. MERRA-2 was the first reanalysis to include interactive and assimilated aerosols. From these, PM 1, 2.5 and 10 records have been computed and contribute to the climate and health sector. The US Center for Disease Control is accessing MERRA-2 data for comparison along with their health data. MERRA-2 Extreme Indices have been computed from the high frequency data (e.g. precipitation maxima, heat waves and fire weather). These have been used to characterize the changing extremes in the United States and around the globe as well as the weather associated with the extremes. The next-generation, MERRA-21C, includes increased resolution (25km) and continues to provide innovative Earth system data. For example, a boundary layer collection will provide constant height level data starting at 100m, continuing up to 4000m. This will provide detailed information about the boundary layer processes that greatly affect the biosphere. Chemical tracers for constituents are included in the system. The ensemble data assimilation is run with 32 members, and the variance for critical variables are stored. We will explore the use of this variance as one measure of uncertainty relevant for applied uses, which has not been included in any MERRA reanalysis. We will discuss use case scenarios developed to engage with the energy and financial sectors, to better understand their needs for climate data. Prospects for further downscaling of reanalyses will also be explored.

Michael Bosilovich↗
NASA NTRS1983

The time development of a blast wave with shock heated electrons

Accurate approximations are presented for the time development of both edge conditions and internal structures of a blast wave with shock heated electrons, and equal ion and electron temperatures at the shock. The cases considered evolve in cavities with power law ambient densities (including the uniform ambient density case) and have negligible external pressure. Account is taken of possible saturation of the thermal conduction flux. The structures evolve smoothly to the adiabatic structures.

Edgar, R. J.↗
NASA NTRS1989

Solar coronal loop heating by cross-field wave transport

Solar coronal arches heated by turbulent ion-cyclotron waves may suffer significant cross-field transport by these waves. Nonlinear processes fix the wave-propagation speed at about a tenth of the ion thermal velocity, which seems sufficient to spread heat from a central core into a large cool surrounding cocoon. Waves heat cocoon ions both through classical ion-electron collisions and by turbulent stochastic ion motions. Plausible cocoon sizes set by wave damping are in roughly kilometers, although the wave-emitting core may be only 100 m wide. Detailed study of nonlinear stabilization and energy-deposition rates predicts that nearby regions can heat to values intermediate between the roughly electron volt foot-point temperatures and the about 100 eV core, which is heated by anomalous Ohmic losses. A volume of 100 times the core volume may be affected. This qualitative result may solve a persistent problem with current-driven coronal heating; that it affects only small volumes and provides no way to produce the extended warm structures perceptible to existing instruments.

Amendt, Peter↗
NASA NTRS2008

The Foggy EUV Corona and Coronal Heating by MHD Waves from Explosive Reconnection Events

In 0.5 arcsec/pixel TRACE coronal EUV images, the corona rooted in active regions that are at the limb and are not flaring is seen to consist of (1) a complex array of discrete loops and plumes embedded in (2) a diffuse ambient component that shows no fine structure and gradually fades with height. For each of two not-flaring active regions, found that the diffuse component is (1) approximately isothermal and hydrostatic and (2) emits well over half of the total EUV luminosity of the active-region corona. Here, from a TRACE Fe XII coronal image of another not-flaring active region, the large sunspot active region AR 10652 when it was at the west limb on 30 July 2004, we separate the diffuse component from the discrete loop component by spatial filtering, and find that the diffuse component has about 60% of the total luminosity. If under much higher spatial resolution than that of TRACE (e. g., the 0.1 arcsec/pixel resolution of the Hi-C sounding-rocket experiment proposed by J. W. Cirtain et al), most of the diffuse component remains diffuse rather being resolved into very narrow loops and plumes, this will raise the possibility that the EUV corona in active regions consists of two basically different but comparably luminous components: one being the set of discrete bright loops and plumes and the other being a truly diffuse component filling the space between the discrete loops and plumes. This dichotomy would imply that there are two different but comparably powerful coronal heating mechanisms operating in active regions, one for the distinct loops and plumes and another for the diffuse component. We present a scenario in which (1) each discrete bright loop or plume is a flux tube that was recently reconnected in a burst of reconnection, and (2) the diffuse component is heated by MHD waves that are generated by these reconnection events and by other fine-scale explosive reconnection events, most of which occur in and below the base of the corona where they are seen as UV explosive events, EUV blinkers, and type II spicules. These MHD waves propagate across field lines and dissipate, heating the plasma in the field between the bright loops and plumes.

Moore, Ron L.↗
NASA NTRS1986

Thermal waves or beam heating in the 1980, November 5 flare

Observations of the temporal evolution of loop BC in soft X rays in the November 5, 1980 flare are reviewed. Calculations are performed to model this evolution. The most consistent interpretation involving a minimum account of energy is the following. Thermal heating near B gives rise to a conduction front which moves out along the loop uninhibited for about 27 s. Beam heating near C gives rise to a second conduction front which moves in the opposite direction and prevents any energy reaching C by thermal conduction from B. Thus both thermal waves and beam heating are required to explain the observed evolution.

Smith, Dean F.↗
NASA NTRS1984

Non-quasi-geostrophic effects in baroclinic waves with latent heat release

A study is conducted for the non-quasi-geostrophic baroclinic wave effects in a saturated atmosphere whose vertical motion is subject to pseudo-adiabatic processes. With respect to the characteristics of energetics for the first-order solution, it is noted that, in the cases of both the dry mode and the first moist mode, the heat transport quantities due to the second-order eddy are small and opposite in sign to their respective transports. The non-quasi-geostrophic effects render the vertical motion field asymmetric in each of the regions involved and enter into the present treatment only as nonlinear terms. The moisture transport terms in the eddy-available potential energy equation is small by comparison to other individual terms in the cyclone scale motion's energetics calculation. This is consistent with the observational results of Smith (1980).

Fichtl, G. H.↗